Group Decision-making Approach Research Articles

Circular economy and circularity supplier selection: a fuzzy group decision approach

The circular economy (CE) seeks to maintain products and materials at their highest utility and value. The organisational and governmental policy have seised onto the CE philosophy to advance socio-economic and environmental development. CE remains an essentially contested concept – making its utilisation as a foundation for managerial and policy decisions challenging. Circularity assessment has not been systematically adopted, especially within supply chain management. Using critical scholarly and practical evidential foundation, we proposed a comprehensive set of metrics that can be utilised in supplier selection, monitoring, and development for circularity. These metrics include the macro, meso, and micro levels. A group decision-making method integrating best-worst method (BWM), regret theory (RT), and dual hesitant fuzzy sets (DHFS) for circular economy and circularity (CEC) supplier evaluation and selection is introduced – providing instrumental value for the identified metrics typology. The proposed BWM-DHFE-RT integrative analytical method can accommodate decisionmaker psychological behaviour under uncertainty while simultaneously capturing divergent or conflicting opinions of different decision-makers. An illustrative business scenario is utilised to demonstrate the application of the proposed method. Though the proposed CE performance metrics and methodology are used for CEC supplier management reasons they have broader applicability. Future research and application directions are discussed.

Multi-criteria Group Decision-Making Approach for Express Packaging Recycling Under Interval-Valued Fuzzy Information: Combining Objective and Subjective Compatibilities

Multi-criteria Group Decision-Making Approach for Express Packaging Recycling Under Interval-Valued Fuzzy Information: Combining Objective and Subjective Compatibilities

A Novel Approach for Multiplicative Linguistic Group Decision Making Based on Symmetrical Linguistic Chi-Square Deviation and VIKOR Method

Most linguistic-based approaches to multi-attribute group decision making (MAGDM) use symmetric, uniformly distributed sets of additive linguistic terms to express the opinions of decision makers. However, in reality, there are also some problems that require the use of asymmetric, uneven, i.e., non-equilibrium, multiplicative linguistic term sets to express the evaluation. The purpose of this paper is to propose a new approach to MAGDM under multiplicative linguistic information. The aggregation of linguistic data is an important component in MAGDM. To solve this problem, we define a chi-square for measuring the difference between multiplicative linguistic term sets. Furthermore, the linguistic generalized weighted logarithm multiple averaging (LGWLMA) operator and linguistic generalized ordered weighted logarithm multiple averaging (LGOWLMA) operator are proposed based on chi-square deviation. On the basis of the proposed two operators, we develop a novel approach to GDM with multiplicative linguistic term sets. Finally, the evaluation of transport logistics enterprises is developed to illustrate the validity and practicality of the proposed approach.

2-Tuple Linguistic Model-based Circular Maturity Assessment Methodology: A Case for Agriculture

There is a rising perception among businesses, society, and institutions that the future of production systems hinges on shifting to a circular economy (CE). The CE approach unlocks new potentials for design and better manufacturing methods. This paper intends to create a Circular Maturity Model guiding organizations to gain a comprehensive understanding of their circularity level and have a powerful insight into their future strategies. A functional methodology is generated with a multi-criterion (MCDM) and group decision-making approach. This methodology intends to evaluate the organization's maturity with five main dimensions and fifteen critical factors. The 2-Tuple Linguistic (2-TL) model is suggested to evaluate the circularity factors to create a more efficient analytical approach to reach the maturity score. The 2-TL model is selected to emphasize the results' interpretability and create a decision-making process closer to human thinking. Plus, an MCDM technique, MARCOS, is suggested for strategy selection to improve maturity. The suggested assessment methodology is applied to an agriculture company in Turkey. Thanks to its critical importance for emerging economies like Turkey, the agriculture sector is selected.

A Hierarchical Stackelberg Game Approach for Large-Scale Group Decision Making Based on Cooperative Incentives

A Hierarchical Stackelberg Game Approach for Large-Scale Group Decision Making Based on Cooperative Incentives

Multigranulation Modified Rough Bipolar Soft Sets and Their Applications in Decision-Making

The classical theory of rough sets (RSs) established by Pawlak, mainly focused on the approximation of sets characterized by a single equivalence relation (ER) over the universe. However, most of the current single granulation structure models cannot meet the user demand or the target of solving problems. Multi-granulation rough sets (MGRS) can better deal with the problems where data might be spread over various locations. In this article, based on modified rough bipolar soft sets (MRBSs), the concept of multi-granulation MRBSs (MGMRBSs) is introduced. A finite collection of bipolar soft sets (BSs) has been used for this purpose. Several important structural properties and results of the suggested model are carefully analyzed. Meanwhile, to measure the uncertainty of MGMRBSs, some important measures associated with MGMRBSs are presented in MGMRBS-approximation space, and some of their interesting properties are examined. In the framework of multi-granulation, we developed optimistic MGMRBSs (OMGMRBSs) and pessimistic MGMRBSs (PMGMRBSs). The relationships among the MGMRBSs, OMGMRBSs, and PMGMRBSs are also established. After that, a novel multi-criteria group decision-making (MCGDM) approach based on OMGMRBSs is developed to solve some problems in decision-making (DM). The basic principles and the detailed steps of the DM model are presented in detail. To demonstrate the applicability and potentiality of the developed model, we give a practical example of a medical diagnosis. Finally, we conduct a comparative study of the proposed MCGDM approach with some existing techniques to endorse the advantages of the proposed model.

Aggregation Operators for Interval-Valued Pythagorean Fuzzy So Set with Their Application to Solve Multi-Attribute Group Decision Making Problem

Interval-valued Pythagorean fuzzy so set (IVPFSS) is a generalization of the interval-valued intuitionistic fuzzy so set (IVIFSS) and interval-valued Pythagorean fuzzy set (IVPFS). The IVPFSS handled more uncertainty comparative to IVIFSS; it is the most signicant technique for explaining fuzzy information in the decision-making process. In this work, some novel operational laws for IVPFSS have been proposed. Based on presented operational laws, two innovative aggregation operators (AOs) have been developed such as interval-valued Pythagorean fuzzy so weighted average (IVPFSWA) and interval-valued Pythagorean fuzzy so weighted geometric (IVPFSWG) operators with their fundamental properties. A multi-attribute group decision-making (MAGDM) approach has been established utilizing our developed operators. A numerical example has been presented to ensure the validity of the proposed MAGDM technique. Finally, comparative studies have been given between the proposed approach and some existing studies. The obtained results through comparative studies show that the proposed technique is more credible and reliable than existing approaches.

Pythagorean Fuzzy Linguistic Power Generalized Maclaurin Symmetric Mean Operators and Their Application in Multiple Attribute Group Decision-Making

As an extension of Pythagorean fuzzy sets and linguistic term sets, Pythagorean fuzzy linguistic sets (PFSs) are powerful to describe decision-making information quantificational and qualitatively, which have received much scholars’ attention. The purpose of this paper is to propose a new multiple attribute group decision-making (MAGDM) approach with Pythagorean fuzzy linguistic (PFL) information. To this end, we firstly analyze the drawbacks of existing operations of PFL numbers and propose new operational rules based on linguistic scale function. The power average (PA) operator is famous for its capacity of reducing the negative influence of unreasonable evaluation values provided by prejudiced decision makers on the decision results. The generalized Maclaurin symmetric mean (GMSM) can not only capture the interrelationship among multiple inputs but also manipulate the effect of related properties by adjusting the parameters. When considering aggregation operators of PFL numbers, we combine PA with GMSM and propose the PFL power generalized Maclaurin symmetric and the PFL power generalized weighted Maclaurin symmetric operators. We also study important properties and special cases of these operators. We continue to investigate MAGDM problems with PFL decision information and propose a novel method to determine the optimal alternative. Finally, we conduct numerical examples to demonstrate the effectiveness of our proposed method. We also attempt to illustrate the advantages and superiorities of the proposed method via comparative analysis.

Decision support system based on complex T-Spherical fuzzy power aggregation operators

<abstract><p>The goal of this research is to develop many aggregation operators for aggregating various complex T-Spherical fuzzy sets (CT-SFSs). Existing fuzzy set theory and its extensions, which are a subset of real numbers, handle the uncertainties in the data, but they may lose some useful information and so affect the decision results. Complex Spherical fuzzy sets handle two-dimensional information in a single set by covering uncertainty with degrees whose ranges are extended from the real subset to the complex subset with unit disk. Thus, motivated by this concept, we developed certain CT-SFS operation laws and then proposed a series of novel averaging and geometric power aggregation operators. The properties of some of these operators are investigated. A multi-criteria group decision-making approach is also developed using these operators. The method's utility is demonstrated with an example of how to choose the best choices, which is then tested by comparing the results to those of other approaches.</p></abstract>

Evaluating the sustainability of smart technology applications in healthcare after the COVID-19 pandemic: A hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence.

During the COVID-19 pandemic, some smart technology applications were more effective than had been expected, whereas some others did not achieve satisfactory performance. Consequently, whether smart technology applications in healthcare are sustainable is a question that warrants investigation. To address this question, a hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence was proposed in this study and then used to evaluate the sustainability of smart technology applications in healthcare. The contribution of this research is its subjective evaluation of the sustainability of smart technology applications followed by correction of the evaluation outcome on the basis of the applications' objective performance during the COVID-19 pandemic. To this end, a fuzzy nonlinear programming model was formulated and optimised. In addition, the impact of several major global events that occurred during the pandemic on the sustainability of smart technology applications was considered. The proposed methodology was applied to evaluate the sustainability levels of eight smart technology applications in healthcare. According to the experimental results, three applications-namely healthcare apps, smartwatches, and remote temperature scanners-are expected to be highly sustainable in healthcare, whereas one application, namely smart clothing, is not.

Group decision approach for product development in the food industry

Group decision approach for product development in the food industry

A fuzzy multicriteria group decision approach for circular business models prioritization

Paper aims Presents a new group decision approach for circular business models (CBMs) prioritization based on circular economy (CE) principles and indicators. Originality It was not found in the literature quantitative approaches to indicate which CBMs can be prioritized towards CE implementation. Research method Fuzzy AHP is applied to calculate the weights of the CE principles. Fuzzy TOPSIS is used to rank the CBMs. A pilot application demonstrates the applicability of the proposed approach. Main findings To perform equally on all dimensions of the circularity indicators, recovery by-products have a greater impact on CE implementation. Focusing on economic indicators, Product as a Service and Recovery By-Products should receive priority. Focusing on social indicators, Product as a Service is the most recommended CBM. Implications for theory and practice It was identified the CBMs that are most suitable for performance improvement regarding CE implementation, according to the organization’s dominant CE principles.

SVNMPR: A new single‐valued neutrosophic multiplicative preference relation and their application to decision‐making process

The aim of the paper is to present the concept of a multiplicative preference relation (MPR) with the features of the single-valued neutrosophic (SVN) set and named as SVN multiplicative preference relation (SVNMPR). The SVN set (SVNS) handles uncertainties more broadly than the intuitionistic fuzzy set by considering the three independent degree systems with a scale rating of 0–1 and is symmetrical about 0.5. However, for asymmetrical distribution, Saaty discusses the 1–9 scale to display the information. Driven by the advantages of the scale of 1–9 and the independence nature of degrees in SVNS, in this paper, we introduce the concept of SVNMPR with ratings values as SVN numbers with range 1 ∕ q − q with q > 1. The main advantages of the proposed relation are the asymmetric and nonuniform distributions of about 1. To complete address this, we divide the stated work into three parts. First, an idea of SVNMPR is added wherein pairwise evaluation values are represented the usage of SVN numbers over a scale of 1 ∕ q − q with q > 1. Also, we outline the idea of the SVN multiplicative preference set (SVNMPS). Second, to rank the proposed SVNMPS, we define a new ranking method with improved score functions by adding the expert's attitude to the analysis. The relationship between the proposed and the existing functions is also derived. Third, we are defining some new operation laws for SVNMPS to preserve the closeness property. On the basis of these laws, we define a number of operators to aggregate the different pairs of the SVNMPSs and to derive a number of relations and inequalities. Finally, a group decision-making approach is established and the practicality of it is demonstrated with a numerical example and compared with a number of existing approaches.

Consensus-based multidimensional due diligence of fintech-enhanced green energy investment projects

The purpose of this study is to provide a hybrid group decision-making approach to evaluate fintech-based financial alternatives for green energy investment projects. First, the multidimensional factors of due diligence for fintech-based financing alternatives of green energy investment projects are identified. In this regard, the balanced scorecard perspectives are considered. Next, consensus-based group decision-making analysis is performed. Second, impact-relation directions for fintech-based financing alternatives of green energy investment projects are defined. For this purpose, the spherical fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology is applied. The novelty of this study is its proposal of a new outlook to due diligence of fintech-project financing for renewable energy investments by using the group and integrated decision-making approaches with spherical fuzzy DEMATEL. The findings indicate that customer expectations are the most essential factor for the revenue sharing and rewarding models. Additionally, this study identified that organizational competency plays the most important role with respect to the peer-to-business debt model. In contrast, the conclusion was reached that financial returns have the greatest importance for the equity sharing model.

Statistics-based approach for large-scale group decision-making under incomplete Pythagorean fuzzy information with risk attitude

As loss in decision sample information occurs during large-scale group decision-making (LSGDM), this paper proposes a statistical estimation approach for handling Pythagorean fuzzy information under the risk attitude of decision-makers (DMs). The DMs are partitioned by risk attitudes (hesitancy degrees) into subgroups. A five-number summary for the subgroups from the incomplete decision information given by the DMs is obtained. The Cornish–Fisher expansion is then applied to estimate the mean, standard variance, and skewness of the decision sample information from the five-number summary. The confidence interval constructed by the skewness is used to obtain the interval-valued Pythagorean fuzzy number (IVPFN) evaluation information of the subgroups. An optimization model based on minimizing the conflicts between the subgroups and the overall group is used to derive the weights of the subgroups. A sorting function of the IVPFNs is used to rank the alternatives. A case study on green credit and a comparison analysis are applied to validate the proposed method.

Policies for Rapid Mitigation of the Crisis’ Effects on Agricultural Supply Chains: A Multi-Criteria Decision Support System with Monte Carlo Simulation

This paper proposes an integrated approach towards rapid decision-making in the agricultural sector aimed at improvement of its resilience. Methodologically, we seek to devise a framework that is able to take the uncertainty regarding policy preferences into account. Empirically, we focus on the effects of COVID-19 on agriculture. First, we propose a multi-criteria decision-making framework following the Pugh matrix approach for group decision-making. The Monte Carlo simulation is used to check the effects of the perturbations in the criteria weights. Then, we identify the factors behind agricultural resilience and organize them into the three groups (food security, agricultural viability, decent jobs). The expert survey is carried out to elicit the ratings in regard to the expected effects of the policy measures with respect to dimensions of agricultural resilience. The case of Lithuania is considered in the empirical analysis. The existing and newly proposed agricultural policy measures are taken into account. The measures related to alleviation of the financial burden (e.g., credit payment deferral) appear to be the most effective in accordance with the expert ratings.

An Intelligent Approach to Evaluating CAD Software Packages Through Hesitant Fuzzy AHP

The evaluation of computer-aided design (CAD) packages among the rising number of alternatives in the market has been a critical process for companies because it directly affects the performance of their design/engineering-related activities in a new product development (NPD) as well as in R&D departments. Moreover, it is seen as an important step towards the computer-integrated manufacturing (CIM) environment. On the other hand, the evaluation process of CAD software packages is a multiple-criteria decision-making (MCDM) problem in the presence of sets of alternatives and evaluation criteria, which should be solved using one of the MCDM methods in the current literature. Therefore, in this study, the analytic hierarchy process (AHP), successfully implemented in both academic research and practices, is used for CAD software selection problem. The method is also used with the group decision-making approach to reach a more reliable solution. Additionally, due to the fact that the crisp pairwise comparison in the traditional AHP method seems to be insufficient and imprecise to capture the right judgments of decision makers (DMs), the hesitant fuzzy linguistic term-sets are integrated with the AHP approach (the hesitant F-AHP) to model the vagueness and uncertainty on judgments of the DMs. This proposed approach is also supported with a user-friendly Excel template that provides an effective tool for companies to evaluate and rank CAD software packages without many tiresome fuzzy comparisons and complex calculations. Moreover, in the paper, a numerical example is presented to demonstrate the effectiveness and applicability of the proposed approach to potential readers and practitioners. In this example, five CAD software options are evaluated based on six criteria by three DMs; and the best alternative is determined.

Evaluating Blockchain requirements for effective digital supply chain management

Collapsing traditional supply chain (SC) nodes into digitally interconnected networks creates several opportunities for efficiency, cost savings, and SC traceability. Blockchain is one of the pioneering transformative 4.0 technologies and is seen as the backbone of digital SCs (DSCs). Several attempts have been made to create explorative designs or models, however, forming a strategic Blockchain vision remains a challenge. This paper specifies those customer needs and design requirements that should be prioritized with a systematic approach for effective Blockchain integration into SCs. The proposed functional methodology provides a practical roadmap for practitioners. It is based on the House of Quality (HoQ) method, with its customer-focused design aspect, utilizing a group decision making (GDM) approach with an incomplete intuitionistic fuzzy relation (IIFR) extension. The GDM approach is employed to overcome the biases of decision making, while IIFRs deal with different or complementary focus centers in qualitative data. The usefulness of this methodology is tested with an application, and the results are validated by experts in the field. The results indicate that a Blockchain-based DSC (BC-DSC) is expected to deliver continuing financial benefit, efficient use of time, and the ability to support. Automation, effective coordination, and conformity are identified as highly critical design requirements. GDM and IIFR are combined for the first time in the literature. The originality of this paper comes from its generation of critical factors geared to obtaining an effective BC-DSC structure and the implementation of HoQ as an efficient tool for design.

A new group decision making approach based on incomplete probabilistic dual hesitant fuzzy preference relations

As an enhanced version of probabilistic hesitant fuzzy sets and dual hesitant fuzzy sets, probabilistic dual hesitant fuzzy sets (PDHFSs) combine probabilistic information with the membership degree and non-membership degree, which can describe decision making information more reasonably and comprehensively. Based on PDHFSs, this paper investigates the approach to group decision making (GDM) based on incomplete probabilistic dual hesitant fuzzy preference relations (PDHFPRs). First, the definitions of order consistency and multiplicative consistency of PDHFPRs are given. Then, for the problem that decision makers (DMs) cannot provide the reasonable associated probabilities of probabilistic dual hesitant fuzzy elements (PDHFEs), the calculation method of the associated probability is given by using an optimal programming model. Furthermore, the consistency level for PDHFPRs is tested according to the weighted consistency index defined by the risk attitude of DMs. In addition, a convergent iterative algorithm is proposed to enhance the unacceptable consistent PDHFPRs’ consistency level. Finally, a GDM approach with incomplete PDHFPRs is established to obtain the ranking of the alternatives. The availability and rationality of the proposed decision making approach are demonstrated by analyzing the impact factors of haze weather.

A Novel Approach for Group Decision Making Based on the Best–Worst Method (G-BWM): Application to Supply Chain Management

Due to the complexity of real-world multi-criteria decision-making (MCDM) issues, analyzing different opinions from a group of decision makers needs to ensure appropriate decision making. The group decision-making methods collect preferences of the decision makers and present the best preferences using mathematical equations. The best–worst method (BWM) is one of the recently introduced MCDM methods that requires fewer pairwise comparisons to obtain the criteria weights than the other MCDM methods. In this research, we develop a novel approach to group decision-making problems based on the BWM called G-BWM. This approach helps us to analyze the preferences of decision makers to carry out democratic decision making using the BWM structure. In order to assess the applicability of the proposed methodology and represent its novelty, two numerical examples from the literature with the application to supply chain management (SCM) (i.e., green supplier selection and supplier development/segmentation) are examined and discussed. The results demonstrate the performance of our proposed G-BWM for group decision making in terms of a large number of decision makers, ease of use and achieving democratic decisions in the decision-making process.